Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia

Abstract

Multi-modal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called “multimodal CCA +joint ICA”, to three or N way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared “mCCA+jICA” with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, temporal lobe and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in brain that are thought to play a role in the clinical expression of schizophrenia. The proposed “mCCA +jICA” method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process.

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

“…Joint independent component analysis (ICA), and linked ICA perform well in spatial decomposition by maximizing the joint independence, but all modalities share a common profile. Combining the strengths of MCCA and jICA, Sui et al developed “MCCA+jICA” [7, 8], a blind fusion algorithm, which successfully captures both multimodal interactions and spatial components at high accuracy to study brain diseases. Other data fusion approaches like independent vector analysis (IVA) generalizes ICA to multiple data sets using the mutual information rate, achieving a similar performance to MCCA+jICA [9].…”

Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders

“…In order to identify the co-altered networks across modalities, we assume that 1) whole brain FNC is a linear mixture of sources in the form of multiple sub-networks (Park et al 2014), 2) whole brain GMD can also be linearly separated into a number of sources as spatial independent components (Xu et al 2009), and 3) disorder incurred functional and structural brain changes are correlated across the source factors of modalities. A joint analysis was applied to FNC and GMD using a data fusion approach called multi-set canonical correlation analysis + joint independent component analysis (mCCA+jICA) (Sui et al 2010; Sui et al 2012b). We expected that the analysis which incorporates FNC and brain structure would reveal changes specific to BD or MDD, and that the abnormalities defined using this approach ultimately may served as potential diagnostic biomarkers with the potential to discriminate these two mood disorders.…”

Simultaneous changes in gray matter volume and white matter fractional anisotropy in Alzheimer’s disease revealed by multimodal CCA and joint ICA

“…Different from the multi-modal techniques, multivariate but single-modal techniques such as scaled subprofile modeling (SSM) can only identify the uncorrelated sources based on single modal imaging data. MCCA and jICA have been validated and applied to identify the structural and functional abnormalities in the brain patterns of patients with schizophrenia (Sui et al, 2011, Sui et al, 2012b). The results showed that mCCA and jICA method is effective to find the function-structure correlation via the strong connection between joint components of the two modalities (Sui et al, 2011).…”